Data Science is uncovering value in the engineering sector through a focus on predictive maintenance, asset management, energy management and other computer vision based approaches that enhance efficiency of industrial assets !
Meenakshi Sundaram, our next pathbreaker, Data Scientist at Siemens (Singapore), works on various data science projects focusing on industrial use cases across multiple industries, including healthcare, mobility, and F&B.
Sundar talks to Shyam Krishnamurthy from The Interview Portal about being fascinated with futuristic technologies like Augmented Reality (AR) and Virtual Reality, and being intrigued by the algorithms behind computer vision libraries which led him to data science.
For students, focus more on practical skills and remain curious about upcoming technologies, and most importantly, you have to be ready to become a lifelong learner.
Sundar, Your background?
I am from the southern part of India. We frequently relocated during my childhood, so I had to do schooling across many cities in Tamil Nadu. This made me more of an introvert. I was practically neither good at sports nor a topper in studies. One thing that naturally came to me was programming. I got introduced to C programming in my secondary schooling. And the funny thing is, I didn’t realize that a filler class would be my future. To date, what I love to do and what gives my life meaning is programming.
What did you do for graduation/post graduation?
Naturally, my first choice was to pursue a bachelor’s degree in computer science, which I did at Amrita Vishwa Vidyapeetham. After a few years of my bachelor’s graduation, I completed my master’s from the National University of Singapore (NUS), specializing in Artificial Intelligence (AI).
What were some of the influences that led you to such an offbeat, unconventional and unique career?
The guiding question for my career is, “What Next?” As I said previously, I started off my learning journey with a couple of programming languages. I practically wanted to use them, but solving typical programming questions was not inspiring for someone like me. So, with the help of thenewboston (https://www.youtube.com/user/thenewboston) tutorials that I got with a Digit magazine, I began learning android development. As a result, I developed and published a couple of android apps along with my friends for my university students during the 2nd year of my bachelor’s.
Again I was stuck with the same question, “What Next?”. Following the usual android and web development, I was fascinated with futuristic technologies like Augmented Reality (AR) and Virtual Reality. This desire for future technologies led me to co-found an AR startup along with my friends in the 3rd year of my bachelor’s. For the next four years, I developed AR and VR apps for a range of smart glasses, from Microsoft Hololens to Oculus.
Back to my favorite question, “What Next?”. This time I was intrigued by the computer vision libraries behind AR apps. I wanted to go deeper and understand the algorithms behind those computer vision libraries. This curiosity of mine is what led me to data science.
How did you plan the steps to get into the career you wanted? Tell us about your career path
Initially, when I was intrigued by the computer vision libraries behind AR apps, I decided to learn about the same during my free time through online courses. I did a couple of online courses related to computer vision and machine learning, including the famous Coursera course from Andrew Ng. Even though the online courses that I did were interesting, they were never enough for me. I wanted to learn more about the same and use them in my day-to-day life. That’s when I decided to do masters specializing in AI.
My master’s helped me to understand the core concepts behind AI. I got exposed to various topics such as machine learning, deep learning, computer vision, and natural language processing during my time at NUS. To master the core AI concepts, just focusing on programming was not enough. I had to improve my mathematical background. I remember revisiting my high school and bachelor’s first-year math during my first semester at NUS. When I first learned mathematical topics such as linear algebra, statistics, probability, integration, and calculus, I never understood why we were learning them in the first place. But now I know why.
After improving my fundamentals, I focused on the core AI topics. My way of learning is always by doing, so I worked on numerous personal and coursework AI projects during my time at NUS. These projects also helped me to build a portfolio that I can showcase. I believe my learnings at NUS guided me to my current role at Siemens.
How did you get your first break?
I don’t think I can specify any particular moment to be my first break. It is a process for me, and I am just following my curiosity.
What were some of the challenges you faced? How did you address them?
As I mentioned before, the most common entry barrier to data science is not focusing on the fundamentals. Don’t just go after the fancy stuff; concentrating on the basics helped me master the core concepts.
Once you are a professional, the next most common challenge in computer science is to keep up with the new technologies. For AI, this is particularly essential to tackle. I try to keep myself up to date in many ways. I regularly attend webinars and events related to AI. I neither quit pursuing online courses nor stopped working on personal projects. I use platforms like LinkedIn and Twitter professionally to follow people related to my field and to get the latest news about the industry. Also, I habitually read AI research papers. There is no one shirt that fits all kinds of solutions; you can find different ways to keep yourself updated. But you have to be ready to become a lifelong learner.
Where do you work now? Tell us about your current role
I am currently a Data Scientist at Siemens Singapore, working on various data science projects focusing on industrial use cases. I have done projects for multiple industries, including healthcare, mobility, and F&B. To give an example, I have worked on a project where we developed a computer vision model that predicts a fish’s weight based on the input image. This biomass estimation model will help fish farm people understand the growth of their fish. Another example would be a project where we developed a model to identify whether a dialysis machine failed or not based on vibration sensors. This failure detection model will also tell which sub-component of the dialysis machine is not working. Typically, my projects would be related to predictive maintenance, asset management, energy management, visual inspection, etc.
If you want to know more about my projects, you can always visit my website (http://msundarv.com/) or LinkedIn page (https://www.linkedin.com/in/msundarv). Or you can contact me directly through LinkedIn. Always happy to share more.
How does your work benefit society?
I believe my work benefits society in numerous ways. I am working on projects focusing on sustainability, where my role is to run industrial assets more efficiently by ensuring that they consume lower energy. I also work on smart city projects like that fish farm project. These projects continue to inspire me and keep me going.
Tell us an example of a specific memorable work you did that is very close to you!
In my first year as a data scientist, I contributed uniquely to one of my projects by bringing in a new research concept that I read from a research paper. This new approach proved to solve our problem statement effectively. And I still remember the moment when I got the initial results by applying this unusual approach.
Your advice to students based on your experience?
Words like passion, curiosity, and focus are not all overrated. If you do what you love, you’ll never work a day in your life.
I really don’t know where my curiosity will take me next.